Digital Signage is an emerging visual advertising medium which utilizes digital displays deployed into public spaces, connected through a wide area network, which display visual advertising messages to individuals within the visual range of the display (“local traffic”). The advertising media takes the form of digital files which are distributed electronically over the network to the remote display system to be run on the display in accordance with some predetermined criteria.
Early implementations of Digital Signage used a simple media loop in which a number of still images (“media segments”) would be displayed in series, each for a period of time, and the cycle would be continuously repeated throughout the day. In this mode, the advertising medium took on the same basic characteristics as traditional static poster advertising except that the ads could be more readily distributed and more highly multiplexed. In spite of these advantages, the display site did not increase the total media value sufficiently to overcome the increased costs of deploying and maintaining the Digital Signs.
In response to this problem, there have been efforts to design “media targeting” systems which tailor the media segments more specifically to the characteristics of the local traffic of a particular display at a given moment, as opposed to running the same loop continuously on all of the displays. By doing so, the total media value of a display site could be raised; if the media targeting is sufficiently robust it could raise the media value of the site enough to overcome the increased costs and thereby support a viable business model.
There are three basic classes of media targeting on a Digital Signage network: 1) those based on the typical demographic characteristics of consumers in the vicinity of the sign with no additional real-time demographics information (Average Demographic Profile), 2) those based on an estimation of the real-time demographics information of consumers in the vicinity of the sign without the benefit of direct consumer identification (Estimated Demographic Profile), and 3) those based on actual real-time consumer demographics information determined by some kind of direct consumer identification method (Actual Demographic Profile).
In general, Actual Demographics Profile systems are preferred in that they more accurately reflect the real-time consumer demographics profile in the vicinity of the sign. However, if an Actual Demographics Profile system is only able to identify a small percentage of the consumers in the vicinity of the sign, then the usefulness of such a system is diminished. Therefore, a robust media targeting system requires not only Actual Demographic Profile capabilities, but the ability to identify a significant percentage of the consumers in the vicinity of the sign.
While Actual Demographics Profile systems are preferred, any method which provides improved demographics profiling capabilities is useful. In order to illustrate this point, consider a Digital Sign in a U.S location which had no additional demographics data associated with it. From an advertiser's perspective, it would be assumed to have the average demographics profile of the U.S population (typically stated as a probabilistic profile). Taking one possible demographics vector, household income, the sign could be modeled by the following table:
PERCENT DISTRIBUTIONTotal100.0Less than $10,0009.5$10,000 to $14,9996.3$15,000 to $19,9996.3$20,000 to $24,9996.6$25,000 to $29,9996.4$30,000 to $34,9996.4$35,000 to $39,9995.9$40,000 to $44,9995.7$45,000 to $49,9995.0$50,000 to $59,9999.0$60,000 to $74,99910.4$75,000 to $99,99910.2$100,000 to $124,9995.2$125,000 to $149,9992.5$150,000 to $199,9992.2$200,000 or more2.4
For an advertiser interested in consumers whose household income was between $25,000 and $35,000, 12.8% of the actual impressions would be of value (sum of “$25,000 to $29,999” and “$30,000 to $34,999” percentage values). Now, assume that the same Digital Sign was characterized as having 18.4% of the consumers meeting this description: the corresponding value to this advertiser was just raised 43%.
The basic requirements for creating a robust media targeting system include: 1) the ability to automatically identify in real-time some individual characteristic of a significant percentage of the individuals comprising the local traffic which can be used to uniquely identify the consumer, 2) the ability to associate the identified individuals with demographics data of interest to advertisers, and 3) the ability to dynamically display media segments based on the profile of the local traffic at that time.
A number of known prior art methods of individual identification require active cooperation on the part of the person to be identified, such as retina scanning for secure area access or swiping a magnetic identification card in a reader. Obviously these technologies would be impractical for use in unrestricted public spaces which represents the majority of the Digital Signage market.
Other known prior art methods of individual identification require passive cooperation on the part of the person to be identified, such as what is described in Boyd/U.S. Pat. No. 6,484,148, the disclosure of which is herein incorporated by reference, wherein unique “signature signals” from wireless devices such as a cell phones carried by users are captured, and then associated to the user through the user's account information. The problem with this kind of identification system is that it requires cooperation by the third party service provider who holds the account information of the user. Because of privacy concerns this information would not likely be released without user consent, or if it were, would not likely withstand public scrutiny. As a result, this kind of system would be limited to users who provide passive cooperation and “opt-in,” thereby limiting the pool of identifiable local traffic below the necessary threshold.
A number of known prior art methods use camera-based visual pattern recognition for individual identification. The state of the art in this field continues to improve the accuracy of the identification process, the ability to identify in real time from a field of multiple individuals, and the ability to identify individuals at farther distances from the camera. All of these trends improve the potential usefulness of visual pattern recognition as an individual identification technology within the field of this invention. However, to date none of the prior art methods describe a media targeting system that can effectively associate the individual identification with meaningful consumer profile information without active or passive cooperation on the part of the user, thereby limiting the system's ability to develop a robust, large-scale database.
The present invention addresses the deficiencies in the prior art and facilitates the development of a robust media targeting system by using visual pattern recognition in conjunction with transaction data collected at the point of purchase.
To draw the distinction between the prior art in visual pattern recognition and this present invention more clearly, the present invention is focused specifically on identification for use in conjunction with a robust media targeting system. It uses visual pattern recognition at a retail point-of-purchase transaction point for initial association with the consumer and the consumer's profile information, and then uses the visual identification indices to deliver targeted advertising on a Digital Signage network at any future time at locations separate from the initial retail point-of-purchase transaction point.
The present invention is therefore novel in its application of visual pattern recognition technology, and unique in its capabilities, in that it addresses all of the requirements for developing a large scale robust media targeting system whereas prior art has not.